Advanced Topics in Artificial intelligence COMP8620
Course overview
Assumed knowledge & required skills
Students are assumed to have solid background knowledge in general computer science (e.g., programming experience; some basic theoretical CS), but no specialist knowledge.
Course description
This is a graduate course that covers advanced topics in Artificial Intelligence. Topics vary from one offering to the next and are likely to be drawn from the following list: planning & scheduling, games, constraint-based reasoning, knowledge compilation, model-based reasoning, decision-making under uncertainty, reinforcement learning.
Course content
See http://cs.anu.edu.au/student/comp4620/.
In 2011, the Advanced AI course will focus on reasoning about discrete event systems,
Discrete event models capture the dynamics of systems that evolve by discrete events. Examples of such systems include digital control systems, as found in embedded and autonomous systems (including robots), telecommunications, etc., but also protocols/workflows, games, and even models of evolution.
System models can be used for many purposes:
- Monitoring & diagnosis: Observing (as far as it can be observed) the behaviour of the system over time, the model is used to infer the state of the system, check if it is functioning correctly, and if not, to determine what faults may have occurred.
- Planning & control: Using the model of system behaviour, devise a plan of control actions to drive/guide the system to a desired goal state or keep it in desired states.
- Verification: Analyse the model to determine if it meets desired criteria.
The course will cover some material related to all these uses, as well as the art of modelling practical systems in discrete-event formalisms.
Textbooks
In 2011: Reading material, consisting mostly of research papers, will be distributed.
Workload
A number of lectures (to be decided; probably 10-30), plus self-study and assignment work.
